app.R

library(shiny)
library(shinycssloaders)
library(wordcloud)
library(ggplot2)
library(shinydashboard)
library(dplyr)
library(tidytext)
library(DT)

source("scrapper/review.R")
source("classifier/naive_bayes.R")

features <- readRDS(features_rds_path)

ui <- dashboardPage(
  
  dashboardHeader(title = "Tripadvisor Restaurant Review"),
  
  dashboardSidebar(
    textInput(
      "url",
      "Enter tripadvisor restauran url", 
      placeholder = "url", 
      value = "https://www.tripadvisor.com/Restaurant_Review-g37209-d739238-Reviews-Weber_Grill_Indianapolis-Indianapolis_Indiana.html"
    ),
    sliderInput(
      "size",
      "Total reviews",
      min = 0,
      max = 1000,
      value = 20
    ),
    fluidPage(
      submitButton("Submit"),
    )
  ),
  
  dashboardBody(
    fluidRow(
      valueBoxOutput("total_review"),
      valueBoxOutput("positive_review"),
      valueBoxOutput("negative_review")
    ),
    fluidRow(
      box(
        title = "Sentimen Analisis",
        solidHeader = T,
        width = 12,
        collapsible = T,
        div(DT::dataTableOutput("table_review") %>% withSpinner(color="#1167b1"), style = "font-size: 70%;")
      ),
    ),
    fluidRow(
      box(title = "Wordcloud",
          solidHeader = T,
          width = 12,
          collapsible = T,
          plotOutput("wordcloud") %>% withSpinner(color="#1167b1")
      ),
    )
  )
)

server <- function(input, output) {
  
  data <- reactive({
    withProgress({
      setProgress(message = "Collecting data", value = 0)
      
      result <- get_restaurant_reviews(input$url, input$size, incProgress)
    })
    
    return(result)
  })
  
  prediction_data <- reactive({
    withProgress({
      setProgress(message = "Predicting sentiment", value = 0)
      
      reviews <- data()$review
      incProgress(1/2)
      prediction <- predict_sentiment(reviews)
      incProgress(1/2)
    })
    prediction$reviewer <- data()$reviewer
    
    return(prediction)
  })
  
  output$table_review <- renderDataTable(datatable({
    prediction_data()
  }))
  
  output$total_review <- renderValueBox({
    valueBox(
      "Total", 
      paste0(nrow(prediction_data()), " review"),
      icon = icon("pen"),
      color = "blue"
    )
  })
  
  output$positive_review <- renderValueBox({
    valueBox(
      "Positive", 
      paste0(nrow(prediction_data() %>% filter(sentiment == "Positive")), " review"),
      icon = icon("smile"),
      color = "green")
  })
  
  output$negative_review <- renderValueBox({
    valueBox(
      "Negative",
      paste0(nrow(prediction_data() %>% filter(sentiment == "Negative")), " review"), 
      icon = icon("frown"),
      color = "red")
  })
  
  output$wordcloud <- renderPlot({
    data.corpus <- clean_data(data()$review)
    wordcloud(data.corpus, min.freq = 30, max.words = 50)
  })
}

shinyApp(ui, server)